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--- |
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base_model: s-nlp/russian_toxicity_classifier |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: tg_comments_model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tg_comments_model |
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This model is a fine-tuned version of [s-nlp/russian_toxicity_classifier](https://huggingface.co/s-nlp/russian_toxicity_classifier) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0519 |
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- Precision: 0.9762 |
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- Recall: 0.9856 |
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- F1: 0.9809 |
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- Accuracy: 0.9817 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.075 | 0.2239 | 300 | 0.0591 | 0.9833 | 0.9731 | 0.9781 | 0.9793 | |
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| 0.0627 | 0.4478 | 600 | 0.0567 | 0.9749 | 0.9843 | 0.9796 | 0.9805 | |
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| 0.0612 | 0.6716 | 900 | 0.0537 | 0.9795 | 0.9821 | 0.9808 | 0.9817 | |
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| 0.0633 | 0.8955 | 1200 | 0.0519 | 0.9762 | 0.9856 | 0.9809 | 0.9817 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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